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检索条件"任意字段=IEEE International Conference on Distributed Computing in Sensor Systems"

共 30822 条记录，以下是1561-1570 订阅

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Supervised Multi-view Feature Selection Via Maximum Margin Criterion Joint distributed Optimization 8

Supervised Multi-view Feature Selection Via Maximum Margin C...

引用

8th ieee international conference on Cloud computing and Intelligence systems, CCIS 2022

作者： Lin, Qiang Jiangnan University School of Business Wuxi214122 China

ISBN: (纸本)9781665477352

This paper proposes a novel supervised multi-view feature selection method via maximum margin criterion (MMC) joint distributed optimization. Firstly, the proposed method integrates the common loss and the local loss of views to establish the global loss. Further, the view-based MMC regularizer and sparse regularizer are constructed, which can give the selected features better class separability. Then the proposed method combines the distributed alternating direction method of multipliers (DADMM) to design a new algorithm to realize the block-based computation. Numerical experiments verify the effectiveness of the proposed method. © 2022 ieee.

关键词： Feature Selection

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SYN Flood Attack Detection and Prevention Technique (SFaDMT) in distributed Environment 4

SYN Flood Attack Detection and Prevention Technique (SFaDMT)...

引用

4th international conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024

作者： Jalil Fufan, Halawati Abd Riskhan, Basheer Hussain, Khalid Jazri, Husin Bin Alajmani, Samah H. Sharma, Komal School of Computing and Informatics Albukhary International University Alor Setar Malaysia School of Computer Science Taylor's University Subang Jaya Malaysia Taif University Department of Information Technology Taif Saudi Arabia Lovely Professional University Division of Research and Development Phagwara144411 India KKR and KSR institute of technology and sciences Andhra Pradesh Guntur India

ISBN: (纸本)9798350353266

In the modern digital landscape, cyber-attacks have become highly advanced and difficult to detect, especially in distributed systems. These Denial of Service (DoS) or distributed Denial of Service (DDoS) attacks lead to resource unavailability for legitimate users, causing financial losses for organizations. To counter these threats, researchers have proposed efficient mitigation techniques. According to Kaspersky's Q3 report, SYN Flood is the most prevalent type of D DoS attack, increasing from 76% to 81% compared to the Q2 report. DDoS attacks can be executed directly or indirectly. In a direct attack, control traffic is sent straight from the attackers to the zombies to target the victim host. Conversely, in an indirect DDoS attack, control traffic is sent indirectly to reflectors by zombies to compromise the target host. Reflectors are uncompromised systems that only respond to requests. To mitigate such attacks, traffic shaping and Pushback techniques are employed. Our research introduces an adaptive heuristic-based method called SYN Flood Attack Detection and Mitigation Technique (SFaDMT) to detect DDoS SYN flood attacks. This study proposes an efficient technique for detecting and blocking SYN attacks. The SFaDMT technique is based on a decision support mechanism. The proposed model is simulated and analyzed in the OMNET simulator, and a comparison is made with the latest technique. The results demonstrate an improvement in detection with the proposed solution. © 2024 ieee.

关键词： Heuristic methods

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Aerial sensor Data Guided Object Detection for Cattle Monitoring in Open Fields

Aerial Sensor Data Guided Object Detection for Cattle Monito...

引用

2023 ieee international conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Yang, Fan Cheng, Irene University of Alberta Department of Computing Science Edmonton Canada

ISBN: (纸本)9798350313154

Cattle detection and stock monitoring in open fields continues to pose a challenge in smart agriculture due to the dynamic motion of the cattle and the complex background scene. With the advancement of sensor technology, e.g., thermal in addition to optical, it is now possible to apply multi-modal data fusion to address the open field detection issue. In this study, we propose a framework to detect a large herd in open fields and compare different object detection methods. In addition, we take advantage of transfer learning and design a new non-maximum suppression method to improve the overall prediction accuracy. We also built a cattle dataset with annotated RGB-thermal frames captured from a drone flying about 30 meters above the ground. Experimental results reveal that the transfer learning method improved the average detection precision from 0.11 to 0.85 using our dataset. While we use cattle detection in this work, our approach can be extended to other object detection applications. © 2023 ieee.

关键词： Drones

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Deinsum: Practically I/O Optimal Multi-Linear Algebra

Deinsum: Practically I/O Optimal Multi-Linear Algebra

引用

international conference for High Performance computing, Networking, Storage and Analysis (HPC)

作者： Ziogas, Alexandros Nikolaos Kwasniewski, Grzegorz Ben-Nun, Tal Schneider, Timo Hoefler, Torsten Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland NextSilicon Tel Aviv Israel

ISBN: (纸本)9781665454445

Multilinear algebra kernel performance on modern massively-parallel systems is determined mainly by data movement. However, deriving data movement-optimal distributed schedules for programs with many high-dimensional inputs is a notoriously hard problem. State-of-the-art libraries rely on heuristics and often fall back to suboptimal tensor folding and BLAS calls. We present Deinsum, an automated framework for distributed multilinear algebra computations expressed in Einstein notation, based on rigorous mathematical tools to address this problem. Our framework automatically derives data movement-optimal tiling and generates corresponding distributed schedules, further optimizing the performance of local computations by increasing their arithmetic intensity. To show the benefits of our approach, we test it on two important tensor kernel classes: Matricized Tensor Times Khatri-Rao Products and Tensor Times Matrix chains. We show performance results and scaling on the Piz Daint supercomputer, with up to 19x speedup over state-of-the-art solutions on 512 nodes.

关键词： automatic programming distributed computing performance analysis linear algebra tensors hardware acceleration

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distributed Generator Rescheduling Optimalization In Sangihe Island Using Unit Commitment Based on Firefly Algorithm 3

Distributed Generator Rescheduling Optimalization In Sangihe...

引用

3rd international conference on Smart Cities, Automation and Intelligent computing systems, ICON-SONICS 2023

作者： Aitana, Darabean Priyadi, Ardyono Lystianingrum, Vita Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Surabaya Indonesia

ISBN: (纸本)9781509062805

Optimization problem in remote areas had been a problem that could not be fully fixed and adjusted the field. This research focuses on optimizing distributed generator rescheduling in Sangihe Island, a remote region with power generation and distribution challenges. The primary aim is to enhance efficiency, reliability, and cost-effectiveness. This research proposes integrating the Unit Commitment (UC) problem with the Firefly Algorithm (FA). Sangihe Island's power system comprises diverse generators, including renewables (photovoltaic) and diesel. To tackle dynamic conditions, we meticulously formulate the UC problem, addressing on/off states and power outputs while considering economic and technical constraints. We use the Firefly Algorithm, renowned for its ability to handle complex problems efficiently. Real-world data validation demonstrates substantial improvements in operational efficiency, cost reduction, and reliability. Comparative analysis confirms the Firefly Algorithm-based Unit Commitment's superiority in addressing the island's unique challenges. From the result, the approach offers an innovative method for Sangihe Island's distributed generator rescheduling, had increased the cost efficiency by IDR15175663 per day or 5.44% more cost efficient for an entire island. © 2023 ieee.

关键词： Cost effectiveness

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A Trusted distributed Crowdsourcing Framework Based on User Preferences

A Trusted Distributed Crowdsourcing Framework Based on User ...

引用

ieee international Performance, computing, and Communications conference (IPCCC)

作者： Sun, Shulin Sun, Lijun Ma, Xinran Pan, Zhenzhen Jin, Hongxin Qingdao Univ Sci & Technol Sch informat Sci & Technol Qingdao Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Qingdao Peoples R China

ISBN: (数字)9781665480185

ISBN: (纸本)9781665480185

As a new computing paradigm to solve large-scale group collaboration problems, crowdsourcing has attracted more and more attention. However, malicious users' participation in crowdsourcing tasks will affect the completion of crowdsourcing tasks or generate malicious evaluations that are inconsistent with the facts, which will reduce the user satisfaction of ordinary users and even lose their trust in the system. In addition, most of the existing crowdsourcing systems rely on the central server and are vulnerable to a single point of failure, affecting users' trust in the system. To solve the above problems, this paper proposes a trusted distributed crowdsourcing framework based on user preferences. Firstly, we propose a trust model of identifying malicious users (IMU) based on reputation value, which can quickly identify all kinds of malicious users. Secondly, the framework is based on an open, transparent, and tamper-proof consortium blockchain to ensure the security and reliability of transaction information, and has developed a complete service process for it. Finally, this paper also takes into account the different preferences of users, and gives priority to the tasks that best meet users' preferences to improve user satisfaction. The proposed framework is deployed on the IBM Hyperledger Fabric. The average transaction confirmation time is 1.4424 s and the average system throughput is 186tps. The experimental results show that the framework can quickly identify malicious users.

关键词： crowdsourcing trust reputation value blockchain user preferences

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Engineering Large Wearable sensor Data towards Digital Measures 8

Engineering Large Wearable Sensor Data towards Digital Measu...

引用

ieee 8th international conference on Big Data Analytics (ICBDA)

作者： Zhang, Hui Ruan, Guangchen Giesting, Regan Miller, Leah Patel, Neel Ji, Ju Yang, Yi Lin Yang, Jian Eli Lilly & Co Digital Hlth Off Indianapolis IN 46285 USA Eli Lilly & Co Adv Analyt & Data Sci Indianapolis IN USA Carmel High Sch Carmel IN USA

ISBN: (纸本)9798350310764

Developing digital biomarkers requires handling unprecedented quantities of digital data generated from digital health technologies that utilize a combination of computing platforms, connectivity, software, and sensors. These collected data need to be transformed and transported into a meaningful and useful format before further being derived into health indicators for understanding disease state and life quality. The unique challenges for this class of data engineering tasks are due to the complexity and volume of the digital data we are handling, the data quality and fidelity required to enable subsequent analysis, and the repeated cycles of trial and error to achieve the desired results. This paper presents a family of systems, pipelines, and methods we have designed and built to facilitate these tasks in a typical digital data engineering lifecycle in the context of digital biomarker development.

关键词： data platform data visualization data engineering data pipeline digital biomarker

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A New Single Ended Calibration Approach of Fiber Optic Based Linear Type Temperature sensor for Tunnels 14

A New Single Ended Calibration Approach of Fiber Optic Based...

引用

14th international conference on Electrical and Electronics Engineering, ELECO 2023

作者： Mulayim, S. Özkan, E. Erkorkmaz, T. Samm Teknoloji Iletişim San. Ve Tic. A.Ş Kocaeli41400 Turkey

ISBN: (纸本)9798350360493

With the widespread use of fiber optic-based distributed sensing technology, many application areas such as temperature detection, leak detection, conveyor temperature monitoring, fire detection in tunnels and passages, temperature monitoring have emerged. Calibration procedures are required in order to convert the signals obtained in these Raman-based systems to absolute temperature and to obtain high sensitivity. In this study, the calibration algorithms of a linear type temperature sensor that detects the temperature with Raman scattering developed for temperature monitoring and fire detection in tunnels and passages are performed. In addition, some filters have been applied to reduce signal noise. © 2023 ieee.

关键词： Temperature sensors

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Maintenance of Model Resilience in distributed Edge Learning Environments 19

Maintenance of Model Resilience in Distributed Edge Learning...

引用

19th international conference on Intelligent Environments, IE 2023

作者： Wang, Qiyuan Anagnostopoulos, Christos Fornes, Jordi Mateo Kolomvatsos, Kostas Vrachimis, Andreas University of Glasgow School of Computing Science United Kingdom University of Lleida Department of Computer Science Spain University of Thessaly Dept. of Informatics and Telecommunications Greece Glasgow University School of Computing Science United Kingdom

ISBN: (纸本)9798350312225

distributed Machine Learning (DML) at the edge of the network involves model learning and inference across networking nodes over distributed data. One type of model learning could be the delivery of predictive analytics services to formulate intelligent environments, however, those environments heavily rely on real-time inference and are significantly influenced by changes in the underlying data (concept drifts). Moreover, the quality of service and availability in DML environments are directly tied to each node's reliability, since such environments are highly susceptible to the impact of node failures. Even if such challenges can be tackled with distributed resilience mechanisms, their effectiveness and efficiency, due to concept drifts, should be maintained to ensure continuous and sustained quality of service. DML systems operate in dynamic environments, thus, they require their models to be updated according to the novel trends embedded in the new data they encounter. We, therefore, introduce several model maintenance mechanisms to ensure resilient DML systems in the long term when concept drifts emerge. We provide a comprehensive experimental evaluation of our resilience maintenance mechanisms over synthetic and real data showcasing their importance and applicability in edge learning environments. © 2023 ieee.

关键词： Quality of service

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Rethinking the distributed DNN Training Cluster Design from the Cost-effectiveness View

Rethinking the Distributed DNN Training Cluster Design from ...

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ieee international conference on High Performance computing and Communications (HPCC)

作者： Zhiquan Lai Yujie Liu Wei Wang Yanqi Hao Dongsheng Li National Key Laboratory of Parallel and Distributed Computing College of Computer National University of Defense Technology Changsha China

As deep learning grows rapidly, model training heavily relies on parallel methods and there exist numerous cluster configurations. However, current preferences for parallel training focus on data centers, overlooking the financial constraints faced by most researchers. To attain the best performance within the cost limitation, we introduce a throughput-cost metric to accurately characterize clusters' cost-effectiveness. Based on this metric, we design a cost-effective cluster featuring the 3090 with NVLink. The experiment results demonstrate that our cluster achieves remarkable cost-effectiveness in various distributed model training schemes.

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